Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	123,563
2	South Dakota	115,447
3	Iowa	92,926
4	Wisconsin	92,831
5	Utah	92,738
6	Rhode Island	91,178
7	Tennessee	90,868
8	Nebraska	89,846
9	Kansas	82,565
10	Idaho	82,537
11	Indiana	81,624
12	Arkansas	81,488
13	Arizona	80,836
14	Wyoming	79,770
15	Illinois	79,768
16	Oklahoma	79,695
17	Montana	79,387
18	Alabama	79,383
19	Nevada	78,293
20	Mississippi	77,781
21	Minnesota	76,181
22	Louisiana	72,718
23	New Mexico	72,404
24	Missouri	71,783
25	Alaska	66,928
26	Florida	66,567
27	Kentucky	66,018
28	Texas	65,945
29	Georgia	65,911
30	South Carolina	65,669
31	California	65,521
32	Delaware	64,645
33	Ohio	64,424
34	New Jersey	63,582
35	Colorado	61,645
36	Massachusetts	59,717
37	Connecticut	56,870
38	North Carolina	56,760
39	New York	55,626
40	Michigan	55,450
41	Pennsylvania	54,602
42	West Virginia	53,568
43	Maryland	48,968
44	Virginia	44,833
45	District of Columbia	43,570
46	New Hampshire	36,229
47	Puerto Rico	35,542
48	Washington	35,329
49	Oregon	28,708
50	Maine	20,551
51	Hawaii	15,969
52	Vermont	13,466

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	1,229
2	Arkansas	1,229
3	Rhode Island	1,221
4	Utah	1,214
5	Tennessee	1,140
6	Louisiana	1,138
7	Alabama	1,028
8	California	964
9	Kentucky	959
10	Massachusetts	919
11	Texas	907
12	Delaware	895
13	Mississippi	875
14	Georgia	846
15	Nevada	846
16	Indiana	833
17	Florida	823
18	South Carolina	818
19	New York	800
20	West Virginia	765
21	Connecticut	759
22	North Carolina	740
23	New Jersey	735
24	Pennsylvania	735
25	Ohio	731
26	Oklahoma	723
27	Wisconsin	723
28	Iowa	721
29	New Mexico	716
30	Kansas	703
31	Idaho	683
32	Nebraska	657
33	Montana	653
34	Illinois	614
35	Virginia	591
36	Missouri	587
37	Colorado	564
38	South Dakota	561
39	Wyoming	530
40	New Hampshire	473
41	Maryland	445
42	Maine	410
43	Michigan	402
44	North Dakota	400
45	District of Columbia	399
46	Alaska	387
47	Minnesota	348
48	Washington	337
49	Vermont	283
50	Oregon	206
51	Hawaii	122
52	Puerto Rico	114

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,211
2	New York	1,988
3	Massachusetts	1,872
4	Rhode Island	1,802
5	North Dakota	1,768
6	Connecticut	1,763
7	South Dakota	1,745
8	Mississippi	1,700
9	Louisiana	1,662
10	Illinois	1,494
11	Michigan	1,388
12	Pennsylvania	1,345
13	Arizona	1,341
14	Indiana	1,310
15	Arkansas	1,300
16	Iowa	1,288
17	New Mexico	1,279
18	District of Columbia	1,144
19	South Carolina	1,099
20	Nevada	1,087
21	Tennessee	1,087
22	Florida	1,046
23	Kansas	1,039
24	Alabama	1,036
25	Georgia	1,024
26	Texas	1,022
27	Maryland	1,021
28	Missouri	1,016
29	Minnesota	1,000
30	Delaware	974
31	Montana	972
32	Nebraska	966
33	Wisconsin	942
34	Colorado	896
35	Idaho	849
36	West Virginia	847
37	Wyoming	844
38	Ohio	809
39	California	722
40	Kentucky	707
41	North Carolina	692
42	Oklahoma	675
43	Virginia	618
44	New Hampshire	608
45	Puerto Rico	497
46	Washington	488
47	Utah	424
48	Oregon	373
49	Alaska	292
50	Maine	286
51	Vermont	248
52	Hawaii	209

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	32
2	Wyoming	29
3	Nebraska	26
4	West Virginia	22
5	Pennsylvania	21
6	Mississippi	19
7	Montana	19
8	Rhode Island	17
9	Michigan	16
10	Missouri	16
11	New Mexico	16
12	Indiana	15
13	Nevada	15
14	New Jersey	15
15	Tennessee	15
16	Kansas	14
17	Alabama	13
18	Arkansas	13
19	California	13
20	Illinois	13
21	North Dakota	12
22	Wisconsin	12
23	Connecticut	11
24	Massachusetts	11
25	New Hampshire	11
26	South Carolina	11
27	South Dakota	11
28	Texas	11
29	Idaho	10
30	Louisiana	10
31	Oklahoma	10
32	Ohio	9
33	Colorado	8
34	Georgia	8
35	Maryland	8
36	Minnesota	8
37	New York	8
38	North Carolina	8
39	Iowa	7
40	Kentucky	7
41	Washington	7
42	Florida	6
43	Maine	6
44	District of Columbia	5
45	Utah	5
46	Vermont	5
47	Virginia	5
48	Delaware	4
49	Oregon	4
50	Puerto Rico	3
51	Alaska	2
52	Hawaii	2

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	275,037	1	99
Dewey	South Dakota	223,693	2	99
Norton	Kansas	221,787	3	99
Lincoln	Arkansas	220,900	4	99
Bon Homme	South Dakota	215,621	5	99
Davidson	Tennessee	105,933	281	91
Richland	South Carolina	68,128	1605	48
York	South Carolina	60,236	1994	36
Orange	California	59,204	2033	35
Pierce	Washington	32,568	2850	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Iron	Wisconsin	6,154	5	99
Davidson	Tennessee	864	1847	41
Richland	South Carolina	839	1892	39
York	South Carolina	669	2189	30
Orange	California	621	2253	28
Pierce	Washington	418	2593	17

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.